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Contains fulltext : 291526.pdf (Publisher’s version ) (Closed access) Drug-resistant tuberculosis is a substantial health-care concern worldwide. Despite culture-based methods being considered the gold standard for drug susceptibility testing, molecular methods provide rapid information about the Mycobacterium tuberculosis mutations associated with resistance to anti-tuberculosis drugs. This consensus document was developed on the basis of a comprehensive literature search, by the TBnet and RESIST-TB networks, about reporting standards for the clinical use of molecular drug susceptibility testing. Review and the search for evidence included hand-searching journals and searching electronic databases. The panel identified studies that linked mutations in genomic regions of M tuberculosis with treatment outcome data. Implementation of molecular testing for the prediction of drug resistance in M tuberculosis is key. Detection of mutations in clinical isolates has implications for the clinical management of patients with multidrug-resistant or rifampicin-resistant tuberculosis, especially in situations when phenotypic drug susceptibility testing is not available. A multidisciplinary team including clinicians, microbiologists, and laboratory scientists reached a consensus on key questions relevant to molecular prediction of drug susceptibility or resistance to M tuberculosis, and their implications for clinical practice. This consensus document should help clinicians in the management of patients with tuberculosis, providing guidance for the design of treatment regimens and optimising outcomes. 01 april 2023

Artificial intelligence (AI) offers the potential to support healthcare delivery, but poorly trained or validated algorithms bear risks of harm. Ethical guidelines stated transparency about model development and validation as a requirement for trustworthy AI. Abundant guidance exists to provide transparency through reporting, but poorly reported medical AI tools are common. To close this transparency gap, we developed and piloted a framework to quantify the transparency of medical AI tools with three use cases. Our framework comprises a survey to report on the intended use, training and validation data and processes, ethical considerations, and deployment recommendations. The transparency of each response was scored with either 0, 0.5, or 1 to reflect if the requested information was not, partially, or fully provided. Additionally, we assessed on an analogous three-point scale if the provided responses fulfilled the transparency requirement for a set of trustworthiness criteria from ethical guidelines. The degree of transparency and trustworthiness was calculated on a scale from 0% to 100%. Our assessment of three medical AI use cases pin-pointed reporting gaps and resulted in transparency scores of 67% for two use cases and one with 59%. We report anecdotal evidence that business constraints and limited information from external datasets were major obstacles to providing transparency for the three use cases. The observed transparency gaps also lowered the degree of trustworthiness, indicating compliance gaps with ethical guidelines. All three pilot use cases faced challenges to provide transparency about medical AI tools, but more studies are needed to investigate those in the wider medical AI sector. Applying this framework for an external assessment of transparency may be infeasible if business constraints prevent the disclosure of information. New strategies may be necessary to enable audits of medical AI tools while preserving business secrets., Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät; 15

There is concern that the COVID-19 pandemic has damaged global childhood tuberculosis management. Quantifying changes in childhood tuberculosis notifications could support more targeted interventions to restore childhood tuberculosis services. We aimed to use time-series modelling to evaluate the impact of COVID-19 on child tuberculosis notifications.Annual tuberculosis case notification data reported to WHO by 215 countries were used to calculate annual notification counts for the years 2014-20, stratified by age groups (0-4, 5-14, and ≥15 years) and sex. We used time-series modelling to predict notification counts for 2020, and calculated differences between these predictions and observed notifications in 2020 for each of the six WHO regions and at the country level for 30 countries with high tuberculosis burden. We assessed associations between these differences and the COVID-19 stringency index, a measure of COVID-19 social impact.From 2014 to 2019, annual tuberculosis notification counts increased across all age groups and WHO regions. More males than females in the 0-4 years age group and ≥15 years age group had notifications in all years from 2014 to 2020 and in all WHO regions. In the 5-14 years age group, more females than males were notified globally in all years, although some WHO regions had higher notifications from males than females. In 2020, global notifications were 35·4% lower than predicted (95% prediction interval -30·3 to -39·9; 142 525 observed vs 220 794 predicted notifications [95% prediction interval 204 509 to 237 078]) for children aged 0-4 years, 27·7% lower (-23·4 to -31·5; 256 398 vs 354 578 [334 724 to 374 431]) in children aged 5-14 years, and 18·8% lower (-15·4 to -21·9; 5 391 753 vs 6 639 547 [6 375 086 to 6 904 007]) for people aged 15 years or older. Among those aged 5-14 years, the reduction in observed relative to predicted notifications for 2020 was greater in males (-30·9% [-24·8 to -36·1]) than females (-24·5% [-18·1 to -29·9]). Among 28 countries with high tuberculosis burden, no association was observed between the stringency of COVID-19 restrictions and the relative difference in observed versus predicted notifications.Our findings suggest that COVID-19 has substantially affected childhood tuberculosis services, with the youngest children most affected. Although children have mostly had fewer severe health consequences from COVID-19 than have adults, they have been disproportionately affected by the effects of the pandemic on tuberculosis care. Observed sex differences suggest that targeted interventions might be required. As countries rebuild health systems following the COVID-19 pandemic, it is crucial that childhood tuberculosis services are placed centrally within national strategic plans.Medical Research Council.

LGBTQ youth, and in particular those of color, are significantly more at risk for experiencing trauma at home and in their community, having school difficulties including bullying and suspensions, and subsequently being involved with the juvenile and criminal justice systems. Research is limited in understanding the pathways these young people take toward youthful and young adult offending and incarceration. The national longitudinal Add Health study data were used to explain how trauma, sexual orientation (gay, bisexual), school experiences, gender, and race impacted juvenile and adult criminal activity and incarceration—looking at a trauma-delinquency-crime link. It was found that females were more likely to experience childhood trauma if they were a person of color, poor, or bisexual; and these traumatic childhood experiences were all direct predictors of adult criminal activity, as was being bisexual or gay. While males were more likely to experience childhood trauma if they were a person of color or poor, but not if they were bisexual or gay, and these traumatic experiences and being bisexual (though not gay) also predicted juvenile delinquency, adult criminal activity, and adult incarceration. Implications and discussion of these and other researcher’s findings are set forth, as well as recommendations.

Recent studies portend a rising global spread and adaptation of human- or healthcare-associated pathogens. Here, we analyse an international collection of the emerging, multidrug-resistant, opportunistic pathogen Stenotrophomonas maltophilia from 22 countries to infer population structure and clonality at a global level. We show that the S. maltophilia complex is divided into 23 monophyletic lineages, most of which harbour strains of all degrees of human virulence. Lineage Sm6 comprises the highest rate of human-associated strains, linked to key virulence and resistance genes. Transmission analysis identifies potential outbreak events of genetically closely related strains isolated within days or weeks in the same hospitals., Multidrug resistance of the opportunistic pathogen Stenotrophomonas maltophilia is an increasing problem. Here, analyzing strains from 22 countries, the authors show that the S. maltophilia complex is divided into 23 monophyletic lineages and find evidence for intra-hospital transmission.

Long diagnostic wait times hinder international efforts to address multi-drug resistance in M. tuberculosis. Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution. However, generalizability and clinical adoption have been limited in part by a lack of interpretability and verifiability, especially in deep learning methods. Here, we present a deep convolutional neural network (CNN) that predicts the antibiotic resistance phenotypes of M. tuberculosis isolates. The CNN performs with state-of-the-art levels of predictive accuracy. Evaluation of salient sequence features permits biologically meaningful interpretation and validation of the CNN’s predictions, with promising repercussions for functional variant discovery, clinical applicability, and translation to phenotype prediction in other organisms.

BackgroundGlobal tuberculosis (TB) drug resistance (DR) surveillance is largely focused on the drug rifampicin. We leveraged public and surveillance M. tuberculosis (Mtb) whole genome sequencing (WGS) data, to generate more comprehensive country-level resistance prevalence estimates (antibiograms) using in silico resistance prediction.MethodsWe curated and quality-controlled Mtb WGS data. We used a validated random forest model to predict phenotypic resistance to twelve drugs and bias-corrected for model performance, outbreak sampling, and resistance oversampling. We validated our estimates using a national DR survey conducted in South Africa.ResultsMtb isolates from 29 countries (n=19,149) met sequence quality criteria. Marginal genotypic resistance estimates overlapped with the South African DR survey for all drugs except isoniazid and second-line injectables that were underestimated (n=3,134); among multi-drug resistant (MDR) TB, estimates overlapped for pyrazinamide and the fluoroquinolones. Globally, mono-resistance to isoniazid was estimated at 10.9% (95% CI: 10.2-11.7%, n = 14,012. Mono-levofloxacin resistance rates were highest in South Asia (Pakistan 3.4% [0.1-11%], n=111 and India 2.8% [0.08-9.4%], n=114). Rates of resistance discordance between isoniazid and ethionamide were high with 74.4% (IQR: 64.5-79.7%) of isoniazid resistant isolates predicted to be ethionamide susceptible. The global susceptibility rate to pyrazinamide and levofloxacin among MDR was 15.1% (95% CI: 10.2-19.9%, n=3,964).ConclusionsThis is the first attempt at global Mtb antibiogram estimation. DR prevalence in Mtb can be reliably estimated using public WGS and phenotypic resistance prediction for key antibiotics. Our results raise concerns about the empiric use of short-course fluoroquinolone regimens for drug susceptible TB in South Asia and suggest that ethionamide is an under-utilized drug in MDR treatment.

Background Multidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly and accurate resistance prediction tools are needed to enable public health and clinical practitioners to rapidly diagnose resistance and inform treatment regimens. Results We present Translational Genomics platform for Tuberculosis (GenTB), a free and open web-based application to predict antibiotic resistance from next-generation sequence data. The user can choose between two potential predictors, a Random Forest (RF) classifier and a Wide and Deep Neural Network (WDNN) to predict phenotypic resistance to 13 and 10 anti-tuberculosis drugs, respectively. We benchmark GenTB’s predictive performance along with leading TB resistance prediction tools (Mykrobe and TB-Profiler) using a ground truth dataset of 20,408 isolates with laboratory-based drug susceptibility data. All four tools reliably predicted resistance to first-line tuberculosis drugs but had varying performance for second-line drugs. The mean sensitivities for GenTB-RF and GenTB-WDNN across the nine shared drugs were 77.6% (95% CI 76.6–78.5%) and 75.4% (95% CI 74.5–76.4%), respectively, and marginally higher than the sensitivities of TB-Profiler at 74.4% (95% CI 73.4–75.3%) and Mykrobe at 71.9% (95% CI 70.9–72.9%). The higher sensitivities were at an expense of ≤ 1.5% lower specificity: Mykrobe 97.6% (95% CI 97.5–97.7%), TB-Profiler 96.9% (95% CI 96.7 to 97.0%), GenTB-WDNN 96.2% (95% CI 96.0 to 96.4%), and GenTB-RF 96.1% (95% CI 96.0 to 96.3%). Averaged across the four tools, genotypic resistance sensitivity was 11% and 9% lower for isoniazid and rifampicin respectively, on isolates sequenced at low depth (< 10× across 95% of the genome) emphasizing the need to quality control input sequence data before prediction. We discuss differences between tools in reporting results to the user including variants underlying the resistance calls and any novel or indeterminate variants Conclusions GenTB is an easy-to-use online tool to rapidly and accurately predict resistance to anti-tuberculosis drugs. GenTB can be accessed online at https://gentb.hms.harvard.edu, and the source code is available at https://github.com/farhat-lab/gentb-site.

Microorganisms living in a biofilm are much more tolerant to antibiotics and antimicrobial substances than planktonic cells are. Thus, the treatment of infections caused by microorganisms living in biofilms is extremely difficult. Nosocomial infections (among others) caused by S. maltophilia, particularly lung infection among CF patients, have increased in prevalence in recent years. The intrinsic multidrug resistance of S. maltophilia and the increased tolerance to antimicrobial agents of its biofilm cells make the treatment of S. maltophilia infection difficult. The significance of our research is based on understanding the common mechanisms involved in biofilm formation of different S. maltophilia isolates, understanding the diversity of biofilm architectures among strains of this species, and identifying the differently regulated processes in biofilm versus planktonic cells. These results will lay the foundation for the treatment of S. maltophilia biofilms., Stenotrophomonas maltophilia is one of the most frequently isolated multidrug-resistant nosocomial opportunistic pathogens. It contributes to disease progression in cystic fibrosis (CF) patients and is frequently isolated from wounds, infected tissues, and catheter surfaces. On these diverse surfaces S. maltophilia lives in single-species or multispecies biofilms. Since very little is known about common processes in biofilms of different S. maltophilia isolates, we analyzed the biofilm profiles of 300 clinical and environmental isolates from Europe of the recently identified main lineages Sgn3, Sgn4, and Sm2 to Sm18. The analysis of the biofilm architecture of 40 clinical isolates revealed the presence of multicellular structures and high phenotypic variability at a strain-specific level. Further, transcriptome analyses of biofilm cells of seven clinical isolates identified a set of 106 shared strongly expressed genes and 33 strain-specifically expressed genes. Surprisingly, the transcriptome profiles of biofilm versus planktonic cells revealed that just 9.43% ± 1.36% of all genes were differentially regulated. This implies that just a small set of shared and commonly regulated genes is involved in the biofilm lifestyle. Strikingly, iron uptake appears to be a key factor involved in this metabolic shift. Further, metabolic analyses implied that S. maltophilia employs a mostly fermentative growth mode under biofilm conditions. The transcriptome data of this study together with the phenotypic and metabolic analyses represent so far the largest data set on S. maltophilia biofilm versus planktonic cells. This study will lay the foundation for the identification of strategies for fighting S. maltophilia biofilms in clinical and industrial settings. IMPORTANCE Microorganisms living in a biofilm are much more tolerant to antibiotics and antimicrobial substances than planktonic cells are. Thus, the treatment of infections caused by microorganisms living in biofilms is extremely difficult. Nosocomial infections (among others) caused by S. maltophilia, particularly lung infection among CF patients, have increased in prevalence in recent years. The intrinsic multidrug resistance of S. maltophilia and the increased tolerance to antimicrobial agents of its biofilm cells make the treatment of S. maltophilia infection difficult. The significance of our research is based on understanding the common mechanisms involved in biofilm formation of different S. maltophilia isolates, understanding the diversity of biofilm architectures among strains of this species, and identifying the differently regulated processes in biofilm versus planktonic cells. These results will lay the foundation for the treatment of S. maltophilia biofilms.